Design and development of a motor imagery based interfaces of wheelchair in a simulated virtual environment
Patients suffering from diseases like motor neuron diseases (MND), or trauma such as spinal cord injury (SCI), and amputation are not able to move. Presented is a work on combining the power wheelchair designed to aid the movement of disabled patient and a Brain Computer Interface (BCI) can be used...
Saved in:
| Format: | Thesis |
|---|---|
| Language: | English |
| Subjects: | |
| Online Access: | http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78027/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78027/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/78027/3/Jackie%20Teh.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Patients suffering from diseases like motor neuron diseases (MND), or trauma such as spinal cord injury (SCI), and amputation are not able to move. Presented is a work on combining the power wheelchair designed to aid the movement of disabled patient and a Brain Computer Interface (BCI) can be used to replace conventional joystick so that it can be controlled without using hands. By using the BCI, the brain signal emanated during Motor Imagery (MI) tasks can be converted into control signal for power wheelchair maneuvering. In this research, five subjects are requested to perform six Kinesthetic Motor Imagery tasks plus one relax task and the Electroencephalography (EEG) signals are recorded. Elliptic filter was used to remove power line noise. The
proposed feature, combined feature of Fractal Dimension with Mel-frequency Cepstral
Coefficients has outperformed the others. It was able to improve the classification
performance to a satisfactory level especially for the subject 3 which yielded relatively
poor result by using four other feature extraction methods. The classifiers network
parameters were experimentally selected and the Levenberg-Marquardt training
algorithm was used to train the networks. The Multilayer Perceptron Neural Network
(MLPNN) outperformed Elman Recurrent Neural Network and Nonlinear
Autoregressive Exogenous model (NARX) with average accuracy of 91.7%. The
developed network models was further tested and evaluated with two simulated virtual
environment created by using MATLAB graphical user interface (GUI). The simulation
results suggested that step by step control is better than continuous control of
wheelchair, and also the proposed feature, combined feature of FD with MFCCs and
MLPNN can be used to classify Motor Imagery signal for directional control of
powered wheelchair. |
|---|